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253 lines
10 KiB
Python
253 lines
10 KiB
Python
# -*- coding: utf-8 -*-
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"""
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This python module implements the Kurganov-Petrova numerical scheme
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for the shallow water equations, described in
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A. Kurganov & Guergana Petrova
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A Second-Order Well-Balanced Positivity Preserving Central-Upwind
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Scheme for the Saint-Venant System Communications in Mathematical
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Sciences, 5 (2007), 133-160.
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Copyright (C) 2016 SINTEF ICT
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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#Import packages we need
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from GPUSimulators import Simulator, Common
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from GPUSimulators.Simulator import BaseSimulator, BoundaryCondition
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import numpy as np
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import ctypes
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#from pycuda import gpuarray
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from hip import hip,hiprtc
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"""
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Class that solves the SW equations using the Forward-Backward linear scheme
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"""
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class KP07 (Simulator.BaseSimulator):
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"""
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Initialization routine
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h0: Water depth incl ghost cells, (nx+1)*(ny+1) cells
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hu0: Initial momentum along x-axis incl ghost cells, (nx+1)*(ny+1) cells
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hv0: Initial momentum along y-axis incl ghost cells, (nx+1)*(ny+1) cells
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nx: Number of cells along x-axis
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ny: Number of cells along y-axis
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dx: Grid cell spacing along x-axis (20 000 m)
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dy: Grid cell spacing along y-axis (20 000 m)
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dt: Size of each timestep (90 s)
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g: Gravitational accelleration (9.81 m/s^2)
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"""
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def hip_check(call_result):
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err = call_result[0]
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result = call_result[1:]
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if len(result) == 1:
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result = result[0]
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if isinstance(err, hip.hipError_t) and err != hip.hipError_t.hipSuccess:
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raise RuntimeError(str(err))
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elif (
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isinstance(err, hiprtc.hiprtcResult)
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and err != hiprtc.hiprtcResult.HIPRTC_SUCCESS
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):
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raise RuntimeError(str(err))
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return result
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def __init__(self,
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context,
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h0, hu0, hv0,
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nx, ny,
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dx, dy,
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g,
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theta=1.3,
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cfl_scale=0.9,
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order=2,
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boundary_conditions=BoundaryCondition(),
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block_width=16, block_height=16):
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# Call super constructor
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super().__init__(context,
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nx, ny,
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dx, dy,
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boundary_conditions,
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cfl_scale,
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order,
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block_width, block_height);
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self.g = np.float32(g)
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self.theta = np.float32(theta)
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self.order = np.int32(order)
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#Get kernels
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# module = context.get_module("cuda/SWE2D_KP07.cu",
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# defines={
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# 'BLOCK_WIDTH': self.block_size[0],
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# 'BLOCK_HEIGHT': self.block_size[1]
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# },
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# compile_args={
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# 'no_extern_c': True,
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# 'options': ["--use_fast_math"],
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# },
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# jit_compile_args={})
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# self.kernel = module.get_function("KP07Kernel")
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# self.kernel.prepare("iifffffiiPiPiPiPiPiPiP")
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kernel_file_path = os.path.abspath(os.path.join('cuda', 'SWE2D_KP07.cu.hip'))
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with open(kernel_file_path, 'r') as file:
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kernel_source = file.read()
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prog = hip_check(hiprtc.hiprtcCreateProgram(kernel_source.encode(), b"KP07Kernel", 0, [], []))
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props = hip.hipDeviceProp_t()
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hip_check(hip.hipGetDeviceProperties(props,0))
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arch = props.gcnArchName
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print(f"Compiling kernel .KP07Kernel. for {arch}")
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cflags = [b"--offload-arch="+arch]
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err, = hiprtc.hiprtcCompileProgram(prog, len(cflags), cflags)
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if err != hiprtc.hiprtcResult.HIPRTC_SUCCESS:
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log_size = hip_check(hiprtc.hiprtcGetProgramLogSize(prog))
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log = bytearray(log_size)
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hip_check(hiprtc.hiprtcGetProgramLog(prog, log))
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raise RuntimeError(log.decode())
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code_size = hip_check(hiprtc.hiprtcGetCodeSize(prog))
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code = bytearray(code_size)
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hip_check(hiprtc.hiprtcGetCode(prog, code))
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module = hip_check(hip.hipModuleLoadData(code))
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kernel = hip_check(hip.hipModuleGetFunction(module, b"KP07Kernel"))
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#Create data by uploading to device
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self.u0 = Common.ArakawaA2D(self.stream,
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nx, ny,
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2, 2,
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[h0, hu0, hv0])
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self.u1 = Common.ArakawaA2D(self.stream,
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nx, ny,
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2, 2,
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[None, None, None])
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#self.cfl_data = gpuarray.GPUArray(self.grid_size, dtype=np.float32)
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data_h = np.empty(self.grid_size, dtype=np.float32)
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num_bytes = data_h.size * data_h.itemsize
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self.cfl_data = hip_check(hip.hipMalloc(num_bytes)).configure(
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typestr="float32",shape=self.grid_size)
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dt_x = np.min(self.dx / (np.abs(hu0/h0) + np.sqrt(g*h0)))
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dt_y = np.min(self.dy / (np.abs(hv0/h0) + np.sqrt(g*h0)))
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dt = min(dt_x, dt_y)
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self.cfl_data.fill(dt, stream=self.stream)
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def substep(self, dt, step_number):
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self.substepRK(dt, step_number)
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def substepRK(self, dt, substep):
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# self.kernel.prepared_async_call(self.grid_size, self.block_size, self.stream,
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# self.nx, self.ny,
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# self.dx, self.dy, dt,
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# self.g,
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# self.theta,
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# Simulator.stepOrderToCodedInt(step=substep, order=self.order),
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# self.boundary_conditions,
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# self.u0[0].data.gpudata, self.u0[0].data.strides[0],
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# self.u0[1].data.gpudata, self.u0[1].data.strides[0],
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# self.u0[2].data.gpudata, self.u0[2].data.strides[0],
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# self.u1[0].data.gpudata, self.u1[0].data.strides[0],
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# self.u1[1].data.gpudata, self.u1[1].data.strides[0],
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# self.u1[2].data.gpudata, self.u1[2].data.strides[0],
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# self.cfl_data.gpudata)
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#launch kernel
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hip_check(
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hip.hipModuleLaunchKernel(
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kernel,
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*self.grid_size,
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*self.block_size,
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sharedMemBytes=0,
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stream=self.stream,
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kernelParams=None,
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extra=( # pass kernel's arguments
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ctypes.c_int(self.nx), ctypes.c_int(self.ny),
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ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(self.dt),
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ctypes.c_float(self.g),
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ctypes.c_float(self.theta),
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Simulator.stepOrderToCodedInt(step=substep, order=self.order),
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ctypes.c_int(self.boundary_conditions),
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ctypes.c_float(self.u0[0].data), ctypes.c_float(self.u0[0].data.strides[0]),
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ctypes.c_float(self.u0[1].data), ctypes.c_float(self.u0[1].data.strides[0]),
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ctypes.c_float(self.u0[2].data), ctypes.c_float(self.u0[2].data.strides[0]),
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ctypes.c_float(self.u1[0].data), ctypes.c_float(self.u1[0].data.strides[0]),
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ctypes.c_float(self.u1[1].data), ctypes.c_float(self.u1[1].data.strides[0]),
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ctypes.c_float(self.u1[2].data), ctypes.c_float(self.u1[2].data.strides[0]),
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self.cfl_data
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)
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)
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)
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hip_check(hip.hipDeviceSynchronize())
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self.u0, self.u1 = self.u1, self.u0
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hip_check(hip.hipModuleUnload(module))
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hip_check(hip.hipFree(cfl_data))
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print("--Launching Kernel .KP07Kernel. is ok")
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def getOutput(self):
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return self.u0
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def check(self):
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self.u0.check()
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self.u1.check()
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# computing min with hipblas: the output is an index
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def min_hipblas(self, num_elements, cfl_data, stream):
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num_bytes = num_elements * np.dtype(np.float32).itemsize
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num_bytes_i = np.dtype(np.int32).itemsize
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indx_d = hip_check(hip.hipMalloc(num_bytes_i))
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indx_h = np.zeros(1, dtype=np.int32)
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x_temp = np.zeros(num_elements, dtype=np.float32)
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#print("--size.data:", cfl_data.size)
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handle = hip_check(hipblas.hipblasCreate())
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#hip_check(hipblas.hipblasGetStream(handle, stream))
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#"incx" [int] specifies the increment for the elements of x. incx must be > 0.
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hip_check(hipblas.hipblasIsamin(handle, num_elements, cfl_data, 1, indx_d))
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# destruction of handle
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hip_check(hipblas.hipblasDestroy(handle))
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# copy result (stored in indx_d) back to the host (store in indx_h)
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hip_check(hip.hipMemcpyAsync(indx_h,indx_d,num_bytes_i,hip.hipMemcpyKind.hipMemcpyDeviceToHost,stream))
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hip_check(hip.hipMemcpyAsync(x_temp,cfl_data,num_bytes,hip.hipMemcpyKind.hipMemcpyDeviceToHost,stream))
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#hip_check(hip.hipMemsetAsync(cfl_data,0,num_bytes,self.stream))
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hip_check(hip.hipStreamSynchronize(stream))
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min_value = x_temp.flatten()[indx_h[0]-1]
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# clean up
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hip_check(hip.hipStreamDestroy(stream))
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hip_check(hip.hipFree(cfl_data))
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return min_value
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def computeDt(self):
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max_dt = self.min_hipblas(self.cfl_data.size, self.cfl_data, self.stream)
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#max_dt = gpuarray.min(self.cfl_data, stream=self.stream).get();
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return max_dt*0.5**(self.order-1)
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